Project description:BackgroundFetal movement (FM) counting is a simple and widely used method of assessing fetal well-being. However, little is known about what women perceive as decreased fetal movement (DFM) and how maternally perceived DFM is reflected in FM charts.MethodsWe analyzed FM counting data from 148 DFM events occurring in 137 pregnancies. The women counted FM daily from pregnancy week 24 until birth using a modified count-to-ten procedure. Common temporal patterns for the two weeks preceding hospital examination due to DFM were extracted from the FM charts using wavelet principal component analysis; a statistical methodology particularly developed for modeling temporal data with sudden changes, i.e. spikes that are frequently found in FM data. The association of the extracted temporal patterns with fetal complications was assessed by including the individuals' scores on the wavelet principal components as explanatory variables in multivariable logistic regression analyses for two outcome measures: (i) complications identified during DFM-related consultations (n = 148) and (ii) fetal compromise at the time of consultation (including relevant information about birth outcome and placental pathology). The latter outcome variable was restricted to the DFM events occurring within 21 days before birth (n = 76).ResultsAnalyzing the 148 and 76 DFM events, the first three main temporal FM counting patterns explained 87.2% and 87.4%, respectively, of all temporal variation in the FM charts. These three temporal patterns represented overall counting times, sudden spikes around the time of DFM events, and an inverted U-shaped pattern, explaining 75.3%, 8.6%, and 3.3% and 72.5%, 9.6%, and 5.3% of variation in the total cohort and subsample, respectively. Neither of the temporal patterns was significantly associated with the two outcome measures.ConclusionsAcknowledging that sudden, large changes in fetal activity may be underreported in FM charts, our study showed that the temporal FM counting patterns in the two weeks preceding DFM-related consultation contributed little to identify clinically important changes in perceived FM. It thus provides insufficient information for giving detailed advice to women on when to contact health care providers. The importance of qualitative features of maternally perceived DFM should be further explored.

Project description:ObjectiveTo assess the value of in utero placental assessment in predicting adverse pregnancy outcome after reported reduced fetal movements (RFM).MethodA non-interventional prospective cohort study of women (N = 300) with subjective RFM at ≥28 weeks' gestation in singleton non-anomalous pregnancies at a UK tertiary maternity hospital. Clinical, sonographic (fetal weight, placental size and maternal, fetal and placental arterial Doppler) and biochemical (maternal serum hCG, hPL, progesterone, PlGF and sFlt-1) assessment was conducted. Multiple logistic regression identified combinations of measurements (models) most predictive of adverse pregnancy outcome (perinatal mortality, birth weight <10th centile, five minute Apgar score <7, umbilical arterial pH <7.1 or base excess <-10, neonatal intensive care admission). Models were compared by test performance characteristics (ROC curve, sensitivity, specificity, positive/negative predictive value, positive/negative likelihood ratios) against baseline care (estimated fetal weight centile, amniotic fluid index and gestation at presentation).Results61 (20.6%) pregnancies ended in adverse outcome. Models incorporating PlGF/sFlt-1 ratio and umbilical artery free loop Doppler impedance demonstrated modest improvement in ROC area for adverse outcome (baseline care 0.69 vs. proposed models 0.73-0.76, p<0.05). However, there was little improvement in other test characteristics (baseline vs. best proposed model: sensitivity 21.7% [95% confidence interval 13.1-33.6] vs. 35.8%% [24.4-49.3], specificity 96.6% [93.4-98.3] vs. 94.7% [90.7-97.0], PPV 61.9% [40.9-79.3] vs. 63.3% [45.5-78.1], NPV 82.8% [77.9-86.8] vs. 85.2% [80.0-89.2], positive LR 6.3 [2.8-14.6] vs. 6.7 [3.4-3.3], negative LR 0.81 [0.71-0.93] vs. 0.68 [0.55-0.83]) and wide confidence intervals. Negative post-test probability remained high (16.7% vs. 14.0%).ConclusionAntenatal placental assessment may improve identification of RFM pregnancies at highest risk of adverse pregnancy outcome but further work is required to understand and refine currently available outcome definitions and diagnostic techniques to improve clinical utility.

Project description:Fetal movements (FM) are an important factor in the assessment of fetal health. However, there is currently no reliable way to monitor FM outside clinical environs. While extensive research has been carried out using accelerometer-based systems to monitor FM, the desired accuracy of detection is yet to be achieved. A major challenge has been the difficulty of testing and calibrating sensors at the pre-clinical stage. Little is known about fetal movement features, and clinical trials involving pregnant women can be expensive and ethically stringent. To address these issues, we introduce a novel FM simulator, which can be used to test responses of sensor arrays in a laboratory environment. The design uses a silicon-based membrane with material properties similar to that of a gravid abdomen to mimic the vibrations due to fetal kicks. The simulator incorporates mechanisms to pre-stretch the membrane and to produce kicks similar to that of a fetus. As a case study, we present results from a comparative study of an acoustic sensor, an accelerometer, and a piezoelectric diaphragm as candidate vibration sensors for a wearable FM monitor. We find that the acoustic sensor and the piezoelectric diaphragm are better equipped than the accelerometer to determine durations, intensities, and locations of kicks, as they have a significantly greater response to changes in these conditions than the accelerometer. Additionally, we demonstrate that the acoustic sensor and the piezoelectric diaphragm can detect weaker fetal movements (threshold wall displacements are less than 0.5 mm) compared to the accelerometer (threshold wall displacement is 1.5 mm) with a trade-off of higher power signal artefacts. Finally, we find that the piezoelectric diaphragm produces better signal-to-noise ratios compared to the other two sensors in most of the cases, making it a promising new candidate sensor for wearable FM monitors. We believe that the FM simulator represents a key development towards enabling the eventual translation of wearable FM monitoring garments.

Project description:We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high level constraints relating to activity structure (i.e., type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high level priors to data driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data driven techniques. We use a transformer based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complimentary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce a robust segmentation and task assessment results on noisy, variable and limited data, which is characteristic of low cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e., lower extremity training for neurological accidents).

Project description:Although transport and slaughter of cattle during the last 10% of the gestation period is prohibited in the European Union, such cattle are sometimes sent for slaughter. The late term pregnancy is usually not recognized by the authorities until the uterus is inspected after slaughter and a near term fetus is observed. Accurate post mortem determination of age of bovine fetuses is therefore of major importance as evidence for the subsequent prosecution of the owner. Fetometric measurements such as crown-rump length (CRL) have been used, but these existing estimators have often been established based on insufficiently described study populations or phenotypes that may have changed in the past decades. Morphological characteristics are also used, but few data are available on the correlation between fetal age and the development of these characteristics. The objectives of this study were to investigate the correlation between fetal age and morphological features of bovine Holstein fetuses and to evaluate the use of these features alone and in combination with fetometric measurements to predict fetal age. We collected fetuses from 274 pregnant Holstein cows with recorded insemination dates slaughtered at a Danish abattoir. Gender, teeth development, occurrence of pigmentation, coat, tactile hair and other morphological features were recorded along with CRL, head width, head length and body weight (BW). The gestational length was calculated based on recorded insemination and slaughter dates, and coefficients of variation (R2) were determined for all recorded variables. Notably, the highest R2 was recorded for head length (0.985) followed by CRL (0.979) and head width (0.974). The categorical (morphological) variables were less informative. When used in multivariable models, they did offer statistically significance, but for practical purposes, limited additional information. A multivariable model including the fetometric variables head length and width in combination with CRL resulted in R2 = 0.99 with predictions that were roughly within +/- 11-12 days in 95% of cases. We conclude that the model based on the fetometric variables only provided the most precise predictions, while combination with morphological features such as eruption of teeth, pigmentation and coat mostly increased the width of the prediction intervals.

Dataset Information

Fetal movement counting for assessment of fetal wellbeing.

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